Machine translation (MT) is the use of software to translate text from one natural language to another. Various methodologies exist in providing a machine translation including rule-based MT and statistics-based MT. Rule-based MT is a general term that denotes machine translation systems based on linguistic information about source and target languages determined from bilingual dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language. Statistics-based MT translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora.
Parsing or syntactic analysis is the process of analyzing a string of symbols in a natural language according to the rules of a formal grammar. Sentence parsing is often performed as a method of understanding the exact meaning of a sentence, sometimes with the aid of devices such as sentence diagrams. It typically emphasizes the importance of grammatical divisions such as subject and predicate.
An ontology formally represents knowledge as a set of concepts within a domain, or specific area of interest such as an industry domain, and the relationships between pairs of concepts. It can be used to model a domain and support reasoning about concepts. An ontology provides a shared vocabulary, which can be used to model a domain, that is, the type of objects and/or concepts that exist, and their properties and relations. An ontology model identifies these object or concepts and defines the relationship between them. Ontologies create a structural framework for organizing information and are used in artificial intelligence, the semantic web, and other areas as a form of knowledge representation about the world or some part of it.